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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Teknoinfo JURTEKSI Jurnal Tekno Kompak JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Algoritma Journal of Engineering and Information Technology for Community Service Jurnal Komputasi
Zaenal Abidin
Universitas Teknokrat Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Efek Peningkatan Jumlah Paralel Korpus Pada Penerjemahan Kalimat Bahasa Indonesia ke Bahasa Lampung Dialek Api Permata Permata; Zaenal Abidin; Farida Ariyani
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2613

Abstract

Experimental observations of the effect of the number of parallel corpus on Indonesian translation into the Lampung dialect api were carried out using the statistical machine translation (SMT) method. SMT utilizes a parallel Indonesian corpus and its translation in the Lampung dialect api as a material for training data. The research strategy was carried out in three ways, namely first strategy with a corpus parallel number of 1000 sentences, the second strategy with a corpus parallel number of 2000 and the third strategy with a corpus parallel number of 3000 sentences. The research starts from the preprocessing phase followed by the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The test results of translating Indonesian sentences intoLampung dialectic api are shown through the accuracy value of Bilingual Evaluation Undestudy (BLEU) obtained in testing 25 single sentences without out-of-vocabulary (OOV) in the first strategy, the second and the third are 21.49%, 59.58% and 73.21%. In testing 25 single sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 23.22%, 44.33% and 68.72%. In testing 25 compound sentences without out-of-vocabulary(OOV) obtained in the first strategy, the second and the third are 18.22%, 39.4% and 69.18%. In testing 25 compound sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 25.94%, 28.22% and 71.94%.
Prediksi Penyakit Asma Menggunakan Naïve Bayes dan Random Forest Berbasis Data Klinis Multivariat Salsabila Ainur Hidayah; Zaenal Abidin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3521

Abstract

Asthma is a chronic respiratory disease that can significantly reduce a patient’s quality of life if it is not detected and treated properly. This study aims to compare the performance of Gaussian Naïve Bayes and Random Forest in predicting asthma using the Asthma Disease Dataset from Kaggle, which contains 2,392 patient records with an imbalanced class distribution: 2,268 non-asthma cases and 124 asthma cases. The steps taken include feature selection, preprocessing using StandardScaler, handling class imbalance with SMOTE applied exclusively to the training data, the classification process, and model evaluation using the metrics accuracy, precision, recall, F1-score, ROC AUC, Cohen’s Kappa, MCC, and 5-Fold Cross Validation. Test results showed that Random Forest achieved the highest accuracy of 0.904 with a precision of 0.080, while Gaussian Naïve Bayes produced a recall of 0.520, an F1-score of 0.134, and an ROC AUC of 0.638. These findings indicate that Random Forest is superior in terms of overall accuracy, while Gaussian Naïve Bayes is more effective in detecting asthma cases in the dataset used. The results of this study can serve as a reference in the development of decision support systems for asthma risk identification, although further validation using more diverse clinical data is still required.
Word Stemming of Lampung Dialect Nyo using N-Gram Stemming Parjito Parjito; Zaenal Abidin; Akmal Junaidi; Wamiliana Wamiliana; Favorisen R. Lumbanraja; Farida Ariyani
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25364

Abstract

Background: Previous translation systems for the Lampung dialect of nyo to Indonesian achieved bilingual evaluation understudy (BLEU) scores below 40%, primarily due to challenges in processing affixed words. Objective: This research aims to perform stemming on affixed words in the Lampung dialect of nyo to enhance the performance of the translation system. Methods: We developed an n-gram stemming approach that reduces affixed words to their base forms by measuring similarity between n-grams using the Dice coefficient method. When similarity exceeds a specified threshold, the system identifies the corresponding base word. Results: Using a dataset of 700 words from the Lampung dialect of nyo, we constructed a comprehensive stemmer covering all affix variations. The optimal threshold was determined to be 0.5, achieving bigram accuracy of 93.86% and trigram accuracy of 89.14%. These accuracy levels demonstrate the method's effectiveness in identifying base word forms, which directly impacts translation quality improvement. Conclusion: N-gram stemming with a 0.5 threshold effectively processes the Lampung dialect of nyo morphology and shows potential for enhancing translation accuracy. This work represents the first comprehensive stemming system specifically designed for the Lampung dialect of nyo, contributing to the development of natural language processing tools for underrepresented regional languages in Indonesia. 
Co-Authors . Wamiliana Ade Surahman Agung Setiawan Ahmad Syihabuddin Akmal Junaidi Aldi Wijaya Andi Nurkholis Anugrayani Bustamin Arliyanti Nurdin Arsya Kharisma Amartya Bernadus Anggo Seno AjiAji Dedi Darwis Diah Ambarwati Dina Amelia Eka Nurhana Endang Hidayat Farida Ariyani Faruq Ulum Gunawan, Rakhmat Dedi Heni Sulistiani Isnain, Auliya Rahman Lumbanraja, Favorisen R Muhamad Odhie Prasetio Nery Siskawati Parjito Parjito Pasha, Donaya Permata Permata Permata Permata Putri Nurlandari Rachmi Marsheilla Aguss Riduwan Napianto Rohmat Indra Borman Salsabila Ainur Hidayah Styawati Styawati Taufik Ridwan, Taufik Theo Kusuma Pamungkas Ulum, Faruk Zulfa Nabila